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Aesthetic preference for art emerges from a weighted integration over hierarchically structured visual features in the brain

Iigaya, Kiyohito and Yi, Sanghyun and Wahle, Iman A. and Tanwisuth, Koranis and O'Doherty, John P. (2020) Aesthetic preference for art emerges from a weighted integration over hierarchically structured visual features in the brain. . (Unpublished)

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It is an open question whether preferences for visual art can be lawfully predicted from the basic constituent elements of a visual image. Moreover, little is known about how such preferences are actually constructed in the brain. Here we developed and tested a computational framework to gain an understanding of how the human brain constructs aesthetic value. We show that it is possible to explain human preferences for a piece of art based on an analysis of features present in the image. This was achieved by analyzing the visual properties of drawings and photographs by multiple means, ranging from image statistics extracted by computer vision tools, subjective human ratings about attributes, to a deep convolutional neural network. Crucially, it is possible to predict subjective value ratings not only within but also across individuals, speaking to the possibility that much of the variance in human visual preference is shared across individuals. Neuroimaging data revealed that preference computations occur in the brain by means of a graded hierarchical representation of lower and higher level features in the visual system. These features are in turn integrated to compute an overall subjective preference in the parietal and prefrontal cortex. Our findings suggest that rather than being idiosyncratic, human preferences for art can be explained at least in part as a product of a systematic neural integration over underlying visual features of an image. This work not only advances our understanding of the brain-wide computations underlying value construction but also brings new mechanistic insights to the study of visual aesthetics and art appreciation.

Item Type:Report or Paper (Discussion Paper)
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URLURL TypeDescription Paper
Iigaya, Kiyohito0000-0002-4748-8432
O'Doherty, John P.0000-0003-0016-3531
Alternate Title:Aesthetic preference for art can be predicted from a mixture of low- and high-level visual features
Additional Information:The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY-NC-ND 4.0 International license. bioRxiv preprint first posted online Feb. 10, 2020. We thank Peter Dayan, Shin Shimojo, Pietro Perona, Lesley Fellows, Avinash Vaidya, Jeff Cockburn and Logan Cross for discussions and suggestions. This work was supported by NIDA grant R01DA040011 and the Caltech Conte Center for Social Decision Making (P50MH094258) to JOD, the Japan Society for Promotion of Science the Swartz Foundation and the Suntory Foundation to KI, and the William H. and Helen Lang SURF Fellowship to IW. Author Contributions: K.I. and J.P.O. conceived and designed the project. K.I., S.Y., I.A.W., K.T., performed experiments and K.I., S.Y., I.A.W., K.T., J.P.O. analyzed and discussed results. K.I., S.Y., I.A.W., J.P.O. wrote the manuscript. The authors declare no competing interests.
Funding AgencyGrant Number
Caltech Conte Center for the Neurobiology of Social Decision MakingUNSPECIFIED
Japan Society for Promotion of Science (JSPS)UNSPECIFIED
Swartz FoundationUNSPECIFIED
Suntory FoundationUNSPECIFIED
Caltech Summer Undergraduate Research Fellowship (SURF)UNSPECIFIED
Record Number:CaltechAUTHORS:20200211-073023783
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Official Citation:Aesthetic preference for art emerges from a weighted integration over hierarchically structured visual features in the brain. Kiyohito Iigaya, Sanghyun Yi, Iman A. Wahle, Koranis Tanwisuth, John P O'Doherty. bioRxiv 2020.02.09.940353; doi:
Usage Policy:No commercial reproduction, distribution, display or performance rights in this work are provided.
ID Code:101215
Deposited By: Tony Diaz
Deposited On:11 Feb 2020 17:18
Last Modified:16 Nov 2021 18:00

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